Climate-induced landscape evolution is resulting in changes to biogeochemical and hydrologi- cal cycling. In the Arctic and sub-Arctic permafrost zones, rising air temperatures are warming, and in some regions even thawing, the frozen ground. Permafrost is a carbon sink. The thermal state of the ground therefore has important implications on carbon exchange with the atmo- sphere. Permafrost thaw mobilises previously sequestered carbon stocks, potentially turning these high latitude regions into a net carbon source. Borehole temperature and active layer depth measurements are the traditional means for monitoring permafrost, however these point measurements cannot easily be extrapolated to the landscape-scale; Earth Observation (EO) data may be used for such purposes. It is widely recognised that changes in the thermal state of permafrost may be associated with longterm changes in surface hydrology. As the ground shifts from a frozen to a thawed state, Arctic lakes display changes in surface extent. Therefore, it has become common practice to explore lake dynamics, using these as indicators of permafrost change; dynamics being the keyword. Surface hydrology is a dynamic process. Discharge studies in the Arctic and sub-Arctic regions are associated with flashy hydrographs. Currently, however, remote sensing of permafrost lake change is done on the scale of decades without explicitly taking seasonality and rapid hydrolog- ical phenology into consideration. To examine the seasonal changes in Arctic surface hydrology on the landscape scale high temporal resolution data are necessary. Synthetic aperture radar instruments are exemplary for such a task. The PhD research focuses on establishing operational techniques for mapping open surface water using synthetic aperture radar data, investigating straightforward raster classification methods and exploring their feasibility by undertaking map accuracy and sensitivity studies (chapter 3). The results are then used to justify error propagation when developing an auto- mated procedure that creates temporal composites of water body extent. These temporal water body classifications are the main EO product used to identify and image seasonal surface water change in Arctic permafrost environments (chapter 4). Furthermore, a terrain-based hydrolog- ical study is undertaken to explore the context of the detected changes and possible links to relief and stream channel network (chapter 5). The aim of this PhD is to demonstrate a new method of dynamic monitoring using the Euro- pean Space Agency’s Envisat Advanced Synthetic Aperture Radar, recommending its incorpo- ration in longterm lake change studies. Technical feasibility is explored, the inherent trade-off vii between spatial and temporal resolution discussed. An automated surface water change de- tection algorithm is developed and its applicability to monitoring spring floods is assessed; noting possible modifications to the drainage system given present-day land-use and land- cover changes that are taking place in the study area, the hydrocarbon-rich Yamalo-Nenets Autonomous District in the North of West Siberia (chapter 6). The key significance of this research is to improve the current knowledge of Arctic lake change by including spring flood events and seasonality in the equation. Therefore, it is strongly believed that this research is of benefit to the entire permafrost community.

Agriculture has an important role in Turkey / hence automated approaches are crucial to maintain sustainability of agricultural activities. The objective of this research is to classify eight crop types cultivated in Karacabey Plain located in the north-west of Turkey using multi-temporal Kompsat-2 and Envisat ASAR satellite data. To fulfill this objective, first, the fused Kompsat-2 images were segmented separately to define homogenous agricultural patches. The segmentation results were evaluated using multiple goodness measures to find the optimum segments. Next, multispectral single-date Kompsat-2 images with the Envisat ASAR data were classified by MLC and SVMs algorithms. To combine the thematic information of the multi-temporal data set, probability maps were generated for each classification result and the accuracies of the thematic maps were then evaluated using segment-based manner. The results indicated that the segment-based approach based on the SVMs method using the multispectral Kompsat-2 and Envisat ASAR data provided the best classification accuracies. The combined thematic maps of June-August and June-July-August provided the highest overall accuracy and kappa value around 92% and 0.90, respectively, which was 4% better than the highest result computed with the MLC method. The produced thematic maps were also evaluated based on field-based manner and the analysis revealed that the classification performances are directly proportional to the size of the agricultural fields.

Multitemporal SAR images have been used successfully for the detection of different types of environmental changes. The detection of urban change using SAR images is complicated due to the special characteristics of SAR images—for example, the existence of speckle and the complex mixture of the urban environment. This thesis investigates the detection of urban changes using SAR images with the following specific objectives: (1) to investigate unsupervised change detection, (2) to investigate reduction of the speckle effect and (3) to investigate spatio-contextual change detection. Beijing and Shanghai, the largest cities in China, were selected as study areas. Multitemporal SAR images acquired by ERS-2 SAR (1998~1999) and Envisat ASAR (2008~2009) sensors were used to detect changes that have occurred in these cities. Unsupervised change detection using SAR images is investigated using the Kittler-Illingworth algorithm. The problem associated with the diversity of urban changes—namely, more than one typology of change—is addressed using the modified ratio operator. This operator clusters both positive and negative changes on one side of the change-image histogram. To model the statistics of the changed and the unchanged classes, four different probability density functions were tested. The analysis indicates that the quality of the resulting change map will strongly depends on the density model chosen. The analysis also suggests that use of a local adaptive filter (e.g., enhanced Lee) removes fine geometric details from the scene. Speckle suppression and geometric detail preservation in SAR-based change detection, are addressed using the nonlocal means (NLM) algorithm. In this algorithm, denoising is achieved through a weighted averaging process, in which the weights are a function of the similarity of small image patches defined around each pixel in the image. To decrease the computational complexity, the PCA technique is used to reduce the dimensionality of the neighbourhood feature vectors. Simple methods to estimate the dimensionality of the new space and the required noise variance are proposed. The experimental results show that the NLM algorithm outperformed traditional local adaptive filters (e.g., enhanced Lee) in eliminating the effect of speckle and in maintaining the geometric structures in the scene. The analysis also indicates that filtering the change variable instead of the individual SAR images is effective in terms of both the quality of the results and the time needed to carry out the computation. The third research focuses on the application of Markov random field (MRF) in change detection using SAR images. The MRF-based change detection algorithm shows limited capacity to simultaneously maintain fine geometric detail in urban areas and combat the effect of speckle noise. This problem has been addressed through the introduction of a global constraint on the pixels’ class labels. Based on NLM theory, a global probability model is developed. The iterated conditional mode (ICM) scheme for the optimization of the MAP-MRF criterion function is extended to include a step that forces the maximization of the global probability model. The experimental results show that the proposed algorithm is better at preserving the fine structural detail, effective in reducing the effect of speckle, less sensitive to the value of the contextual parameter, and less affected by the quality of the initial change map compared with traditional MRF-based change detection algorithm. / <p>QC 20130610</p>